Hands on Exercise 10a - Processing and Visualizing Flow Data

Author

Kieren Chua

Published

Invalid Date

Modified

November 3, 2024

Part 1 : Reading Packages and Data

pacman::p_load(tmap, sf, DT, stplanr, tidyverse)
odbus <- read_csv("data/aspatial/origin_destination_bus_202210.csv")
Rows: 5122925 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): YEAR_MONTH, DAY_TYPE, PT_TYPE
dbl (4): TIME_PER_HOUR, ORIGIN_PT_CODE, DESTINATION_PT_CODE, TOTAL_TRIPS

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(odbus)
Rows: 5,122,925
Columns: 7
$ YEAR_MONTH          <chr> "2022-10", "2022-10", "2022-10", "2022-10", "2022-…
$ DAY_TYPE            <chr> "WEEKDAY", "WEEKENDS/HOLIDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR       <dbl> 10, 10, 7, 11, 16, 16, 20, 7, 7, 11, 11, 8, 11, 11…
$ PT_TYPE             <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE      <dbl> 65239, 65239, 23519, 52509, 54349, 54349, 43371, 8…
$ DESTINATION_PT_CODE <dbl> 65159, 65159, 23311, 42041, 53241, 53241, 14139, 9…
$ TOTAL_TRIPS         <dbl> 2, 1, 2, 1, 1, 4, 1, 3, 1, 5, 2, 5, 15, 40, 1, 1, …
odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE) 

Part 2 : Extracting the study data

Get Morning Commute Flows between 6am and 9am

odbus6_9 <- odbus %>%
  filter(DAY_TYPE == "WEEKDAY") %>%
  filter(TIME_PER_HOUR >= 6 &
           TIME_PER_HOUR <= 9) %>%
  group_by(ORIGIN_PT_CODE,
           DESTINATION_PT_CODE) %>%
  summarise(TRIPS = sum(TOTAL_TRIPS))
`summarise()` has grouped output by 'ORIGIN_PT_CODE'. You can override using
the `.groups` argument.
datatable(odbus6_9)
Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

We can keep the data for future use

write_rds(odbus6_9, "data/rds/odbus6_9.rds")

Re-read the data

odbus6_9 <- read_rds("data/rds/odbus6_9.rds")

Part 3: Working with Geospatial Data

busstop <- st_read(dsn = "data/geospatial",
                   layer = "BusStop") %>%
  st_transform(crs = 3414)
Reading layer `BusStop' from data source 
  `C:\Users\Admin\Desktop\SMU\ISSS626\ISSS626-KierenChua\HandsonEx\HandsonEx10\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 5159 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21
mpsz <- st_read(dsn = "data/geospatial",
                   layer = "MPSZ-2019") %>%
  st_transform(crs = 3414)
Reading layer `MPSZ-2019' from data source 
  `C:\Users\Admin\Desktop\SMU\ISSS626\ISSS626-KierenChua\HandsonEx\HandsonEx10\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84

Verify that the crs of mpsz is 3414

mpsz
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM
First 10 features:
                 SUBZONE_N SUBZONE_C       PLN_AREA_N PLN_AREA_C       REGION_N
1              MARINA EAST    MESZ01      MARINA EAST         ME CENTRAL REGION
2         INSTITUTION HILL    RVSZ05     RIVER VALLEY         RV CENTRAL REGION
3           ROBERTSON QUAY    SRSZ01  SINGAPORE RIVER         SR CENTRAL REGION
4  JURONG ISLAND AND BUKOM    WISZ01  WESTERN ISLANDS         WI    WEST REGION
5             FORT CANNING    MUSZ02           MUSEUM         MU CENTRAL REGION
6         MARINA EAST (MP)    MPSZ05    MARINE PARADE         MP CENTRAL REGION
7                   SUDONG    WISZ03  WESTERN ISLANDS         WI    WEST REGION
8                  SEMAKAU    WISZ02  WESTERN ISLANDS         WI    WEST REGION
9           SOUTHERN GROUP    SISZ02 SOUTHERN ISLANDS         SI CENTRAL REGION
10                 SENTOSA    SISZ01 SOUTHERN ISLANDS         SI CENTRAL REGION
   REGION_C                       geometry
1        CR MULTIPOLYGON (((33222.98 29...
2        CR MULTIPOLYGON (((28481.45 30...
3        CR MULTIPOLYGON (((28087.34 30...
4        WR MULTIPOLYGON (((14557.7 304...
5        CR MULTIPOLYGON (((29542.53 31...
6        CR MULTIPOLYGON (((35279.55 30...
7        WR MULTIPOLYGON (((15772.59 21...
8        WR MULTIPOLYGON (((19843.41 21...
9        CR MULTIPOLYGON (((30870.53 22...
10       CR MULTIPOLYGON (((26879.04 26...
mpsz <- write_rds(mpsz, "data/rds/mpsz.rds")

Part 4 : Geospatial data wrangling

busstop_mpsz <- st_intersection(busstop, mpsz) %>%
  select(BUS_STOP_N, SUBZONE_C) %>%
  st_drop_geometry()
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
datatable(busstop_mpsz)
write_rds(busstop_mpsz, "data/rds/busstop_mpsz.rds")  

We do left join to match each time to a bus stop

od_data <- left_join(odbus6_9 , busstop_mpsz,
            by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
  rename(ORIGIN_BS = ORIGIN_PT_CODE,
         ORIGIN_SZ = SUBZONE_C,
         DESTIN_BS = DESTINATION_PT_CODE)
Warning in left_join(odbus6_9, busstop_mpsz, by = c(ORIGIN_PT_CODE = "BUS_STOP_N")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 55491 of `x` matches multiple rows in `y`.
ℹ Row 161 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

We then check for duplicating records

duplicate <- od_data %>%
  group_by_all() %>%
  filter(n()>1) %>%
  ungroup()

There are indeed some duplicated records, we will need to get rid of them

od_data <- unique(od_data)
od_data <- left_join(od_data , busstop_mpsz,
            by = c("DESTIN_BS" = "BUS_STOP_N")) 
Warning in left_join(od_data, busstop_mpsz, by = c(DESTIN_BS = "BUS_STOP_N")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 74 of `x` matches multiple rows in `y`.
ℹ Row 1379 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.
duplicate <- od_data %>%
  group_by_all() %>%
  filter(n()>1) %>%
  ungroup()
od_data <- unique(od_data)
od_data <- od_data %>%
  rename(DESTIN_SZ = SUBZONE_C) %>%
  drop_na() %>%
  group_by(ORIGIN_SZ, DESTIN_SZ) %>%
  summarise(MORNING_PEAK = sum(TRIPS))
`summarise()` has grouped output by 'ORIGIN_SZ'. You can override using the
`.groups` argument.

Now save and re-read the data

write_rds(od_data, "data/rds/od_data_fii.rds")
od_data_fii <- read_rds("data/rds/od_data.rds")

Part 5 : Visualizing Spatial Interaction

Remove intrazonal flows

od_data_fij <- od_data[od_data$ORIGIN_SZ!=od_data$DESTIN_SZ,]
write_rds(od_data_fij, "data/rds/od_data_fij.rds")
od_data_fij <- read_rds("data/rds/od_data_fij.rds")

Creating Desire Lines

flowLine <- od2line(flow = od_data_fij, 
                    zones = mpsz,
                    zone_code = "SUBZONE_C")
Creating centroids representing desire line start and end points.
write_rds(flowLine, "data/rds/flowLine.rds")
flowLine <- read_rds("data/rds/flowLine.rds")

Visualizing Desire Lines

tmap_mode("plot")
tmap mode set to plotting
tm_shape(mpsz) +
  tm_polygons() +
flowLine %>%  
tm_shape() +
  tm_lines(lwd = "MORNING_PEAK",
           style = "quantile",
           scale = c(0.1, 1, 3, 5, 7, 10),
           n = 6,
           alpha = 0.3)
Warning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object length
Legend labels were too wide. Therefore, legend.text.size has been set to 0.66. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

Too many flows, so we should focus on the more important ones, > 5000.

tmap_mode("plot")
tmap mode set to plotting
tm_shape(mpsz) +
  tm_polygons() +
flowLine %>%  
  filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
  tm_lines(lwd = "MORNING_PEAK",
           style = "quantile",
           scale = c(0.1, 1, 3, 5, 7, 10),
           n = 6,
           alpha = 0.3)
Warning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object length
Legend labels were too wide. Therefore, legend.text.size has been set to 0.66. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.